Course Syllabus

Data Visualization

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Semester & Location:

Spring/Fall - Stockholm
Type & Credits:

Elective Course - 3 credits

Major Disciplines:

Computer Science, Information Science, Business, Design
Prerequisites

One course in mathematics at university level
Faculty Members:

TBA
Program Contact:

Natalia Landázuri, Ph.D.
Time & Place: TBA

Course Description

Visual representations of vast and complex arrays of data can change the way we understand and respond to everyday life. Interactive maps of the USA presidential election have allowed us to track vote counting and predict potential outcomes in real time. Visualizations of changes in temperature over time in relation to production of greenhouse gases and natural events have allowed us to understand human impact on climate change and simulate potential future scenarios. Genomic data visualizations continuously help to unveil DNA mutations directly related to certain diseases. Using a hands-on approach, this course utilizes computational tools to transform raw data into interactive visual representations that are easy to access, understand, analyze and reflect upon with the goal to provide insight and augment the cognitive capacity of both domain experts and lay people.
In this course, students will learn theory and state-of-the-art design techniques to visually extract insight from data. Students will learn the required technical skills to create powerful interactive visualizations using the most power tools in the visual analytics industry such as Tableau and Qlikview. This course is an introduction to the field that does not require programming knowledge. The course is for everyone interested in efficiently extracting insight from data through visual analysis.

 

Course goals and learning outcomes

Data Science

  • Understanding data, basics of data science.
  • Raw data, cleanup and sources: understand the origins and quality.
  • The data science workflow: acquire, prepare, explore, analyze, and visualize.

Visualization and human-centered design

  • Connect humans and data: visualization techniques and practices.
  • The visualization pipeline: raw data --> data models --> visual structures --> view transformations --> analysis and insight
  • Principles of graphic design.
  • Respect the reader: How to design for a target audience.
  • Know your data: avoid misuse and deception.
  • Morals and ethics of data in acquisition and use.

Technical skills

  • Data modeling and curation
  • Data mapping to visual structures
  • Interactive view transformations
  • Visual Analytics
  • Choosing the correct tool: How to assess the need and usefulness of different tools to aid working with data and visualizing it.
  • If students wish to program their own visualization in Vega or D3, they will be encouraged and supported.

Examples of possible software tools

D3 Example Image 

Example of an interactive visualization: the relation of Marvel characters in movies. Source: Nadieh Bremers Block

 

Visits and field studies

Possible visits can include:

 

Approach to learning

We will use various learning methods, including interactive lectures, class discussions, critical analysis of reading material, field studies, and project-based learning to build a final project. All sessions combine active learning with short presentations from the lecturer and from students as well. We will learn through exercises with existing visualizations. We will have analytical tasks and present results for critical and constructive feedback. Furthermore, students will create their own visualizations and provide constructive peer feedback to each other. We will have short quizzes to discuss the reading material for each session. The pace and specific activities planned for certain days may change depending on the interest of the students.

 

Expectations of the students

  • Students should participate during lectures, peer-led oral presentations, discussions, group work and exercises.
  • Laptops may be used for note‐taking, fact‐checking, or assignments in the classroom, but only when indicated by the instructor. At all other times laptops and electronic devices should be put away during class time.
  • Reading must be done prior to the class session. A considerable part of the class depends on class discussions.
  • Students need to be present, arrive on time and participate to receive full credit. The final grade will be affected by unexcused absences and lack of participation. The participation grade will be reduced by 10 points (over 100) for every unexcused absence. Remember to be in class on time!
  • Classroom etiquette includes being respectful of other opinions, listening to others and entering a dialogue in a constructive manner.
  • Students are expected to ask relevant questions in regards to the material covered.

 

Evaluation and Grading 

To be eligible for a passing grade in this class, all of the assigned work must be completed.

Students are expected to turn in all the assignments on the due date. If an assignment is turned in after the due date, the grade of the assignment will be reduced by 10 points (over 100) for each day the submission is late.

The factors influencing the final grade and the proportional importance of each factor is shown below:

Assignment

Percent

Active participation

10%

Reading quizzes

10%

In-class exercises

20%

Short projects

20%

Course project: proposal 

10%

Course project: Hello World Demo

10%

Course project: presentation

10%

Course project: report

10%

 

Readings

Books

  1. Introduction to information visualization, Riccardo Mazza - Read chapter 1, Introduction to Visual Representations. Entire book.
  2. Information Visualization, in Handbook of Human Factors and Ergonomics, ch43 - Chris North. Chapter 43.
  3. Ware, Colin. Information visualization: perception for design. Morgan Kaufmann, 4th ed., 2020. (Chapters 2 - 4)

Papers

  1. The Eyes Have It, a paper by Ben Shneiderman. It is in Chapter 8 of The Craft of Information Visualization: Readings and Reflections by Bederson, Benjamin B and Ben Shneiderman, 2003
  2. The Challenge of Information Visualization Evaluation by Catherine Plaisant. AVI '04: Proceedings of the working conference on Advanced visual interfaces, May 2004 Pages 109–116 
  3. Other papers may be assigned as needed

 

Faculty

TBA

 

Academic Regulations  

Please make sure to read the Academic Regulations on the DIS website. There you will find regulations on:

 

DIS - Study Abroad in Scandinavia - www.DISabroad.org

Course Summary

  1. Introduction and motivation

    1. What is data visualization?
    2. What can it do for you?

 

Part I: Using visualizations

  1. The visualization pipeline and the 3 big questions
    1. Who is the user?
    2. What is the data?
    3. What are the tasks?
  2. The user
    1. Typical users and tasks
    2. Visual perception part 1
      • Physiology of the eye
      • Construction of vision
  3. Visual perception part 2
    1. Preattentive features
    2. Gestalt principles
  4. Visual perception part 3
    1. Color
  5. The data
    1. Ethics
    2. Collection
    3. Handling
    4. Curation
      • pre-processing
    5. APIs
    6. Sources
      • Open
      • Paid
  6. The tasks
    1. High-level tasks
      • Analysis
      • Synthesis
      • Presentation and communication
      • Persuasion
      • Discovery
      • Tracking interaction history
  7. Course Project Proposals
  8. Mid-level tasks
    1. Identification of trends
    2. Pattern recognition
    3. Forecasting
    4. Correlation
    5. Hypothesis generation
  9. Low-Level tasks
    1. Determine range
    2. Find maxima and minima
    3. Sort
    4. Filter
    5. Zoom
    6. Rotate
    7. Project
    8. Reduce dimensionality
    9. Identify outliers

 

Part II: Building visualizations

  1. Data transformations
  2. Data models
  3. Course Project Hello-World Demos
  4. Data mappings
  5. Visualization literacy
  6. Visual Structures
    1. 2D
    2. 3D
    3. ND
    4. Temporal
    5. Trees
    6. Graphs
  7. Interactive view transformations
  8. Back to the user and tasks
  9. Evaluation
  10. Task-based laboratory user studies
  11. Deployment studies

 

Part III: Course project presentations

  1. Course Project Final Presentations Part 1
  2. Course Project Final Presentations Part 2